Overview

Dataset statistics

Number of variables84
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory189.0 B

Variable types

Numeric15
Categorical69

Warnings

education_5 has constant value "0" Constant
education_6 has constant value "0" Constant
bill_amt1 is highly correlated with bill_amt2 and 4 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 3 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 4 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
bill_amt1 is highly correlated with bill_amt2 and 11 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 11 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 11 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 13 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 12 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 9 other fieldsHigh correlation
pay_amt1 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_amt2 is highly correlated with bill_amt3 and 5 other fieldsHigh correlation
pay_amt3 is highly correlated with bill_amt4 and 7 other fieldsHigh correlation
pay_amt4 is highly correlated with bill_amt4 and 6 other fieldsHigh correlation
pay_amt5 is highly correlated with bill_amt4 and 5 other fieldsHigh correlation
pay_amt6 is highly correlated with bill_amt5 and 4 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with bill_amt1 and 8 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with bill_amt1 and 9 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
bill_amt1 is highly correlated with bill_amt2 and 4 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 6 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 6 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_amt1 is highly correlated with bill_amt2High correlation
pay_amt2 is highly correlated with bill_amt3High correlation
pay_amt4 is highly correlated with bill_amt5High correlation
pay_amt5 is highly correlated with bill_amt6High correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 3 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with bill_amt2 and 6 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with bill_amt3 and 6 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 4 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
pay_5_2 is highly correlated with pay_4_2 and 4 other fieldsHigh correlation
pay_6_4 is highly correlated with pay_4_4 and 2 other fieldsHigh correlation
marriage_1 is highly correlated with age and 1 other fieldsHigh correlation
default_payment_next_month is highly correlated with pay_1_2High correlation
pay_2_0 is highly correlated with pay_1_1 and 10 other fieldsHigh correlation
pay_6_3 is highly correlated with pay_5_4 and 1 other fieldsHigh correlation
pay_4_2 is highly correlated with pay_5_2 and 4 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_2_3 is highly correlated with pay_1_4High correlation
bill_amt5 is highly correlated with pay_amt3 and 6 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 3 other fieldsHigh correlation
pay_4_3 is highly correlated with pay_3_4High correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_-1 is highly correlated with pay_2_-1 and 8 other fieldsHigh correlation
age is highly correlated with marriage_1 and 1 other fieldsHigh correlation
pay_3_4 is highly correlated with pay_4_3 and 3 other fieldsHigh correlation
pay_1_2 is highly correlated with default_payment_next_month and 1 other fieldsHigh correlation
pay_4_4 is highly correlated with pay_6_4 and 5 other fieldsHigh correlation
pay_amt5 is highly correlated with bill_amt3 and 1 other fieldsHigh correlation
pay_amt3 is highly correlated with bill_amt5 and 8 other fieldsHigh correlation
education_3 is highly correlated with education_2High correlation
pay_5_8 is highly correlated with pay_6_8 and 1 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt5 and 6 other fieldsHigh correlation
pay_1_1 is highly correlated with pay_2_0 and 2 other fieldsHigh correlation
pay_5_3 is highly correlated with pay_4_4High correlation
education_2 is highly correlated with education_3 and 1 other fieldsHigh correlation
pay_3_2 is highly correlated with pay_5_2 and 4 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt5 and 8 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_2_0 and 8 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt5 and 6 other fieldsHigh correlation
pay_1_7 is highly correlated with pay_4_4 and 2 other fieldsHigh correlation
pay_1_5 is highly correlated with pay_2_4High correlation
pay_5_4 is highly correlated with pay_6_4 and 5 other fieldsHigh correlation
pay_6_5 is highly correlated with pay_5_6High correlation
pay_2_4 is highly correlated with pay_1_5 and 1 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_2_5 is highly correlated with pay_3_4 and 1 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
pay_2_6 is highly correlated with pay_4_4 and 2 other fieldsHigh correlation
pay_amt1 is highly correlated with pay_amt3 and 2 other fieldsHigh correlation
pay_1_4 is highly correlated with pay_2_3High correlation
pay_6_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_3_4 and 1 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_5_2 and 9 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 8 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2 and 3 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_5_-1 and 6 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_2_0 and 8 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt5 and 6 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 9 other fieldsHigh correlation
pay_4_0 is highly correlated with pay_2_0 and 9 other fieldsHigh correlation
pay_3_0 is highly correlated with pay_2_0 and 10 other fieldsHigh correlation
pay_5_6 is highly correlated with pay_6_5High correlation
pay_2_2 is highly correlated with pay_5_2 and 6 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_5 is highly correlated with pay_4_4 and 2 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_5 is highly correlated with pay_6_4High correlation
pay_amt2 is highly correlated with pay_amt5 and 3 other fieldsHigh correlation
limit_bal is highly correlated with bill_amt5 and 5 other fieldsHigh correlation
marriage_2 is highly correlated with marriage_1 and 1 other fieldsHigh correlation
pay_4_-1 is highly correlated with pay_2_0 and 8 other fieldsHigh correlation
education_1 is highly correlated with education_2High correlation
bill_amt1 is highly correlated with bill_amt5 and 6 other fieldsHigh correlation
pay_amt4 is highly correlated with pay_amt3 and 1 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_2_0 and 7 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_6_3 and 4 other fieldsHigh correlation
pay_5_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_6_7 is highly correlated with pay_4_7 and 4 other fieldsHigh correlation
female is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 3 other fieldsHigh correlation
pay_6_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
marriage_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
default_payment_next_month is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_5_0 and 7 other fieldsHigh correlation
pay_1_6 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_6_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_2 is highly correlated with pay_5_2 and 3 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_2_0 and 7 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 6 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2 and 2 other fieldsHigh correlation
pay_3_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_2_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_4_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_6_0 and 6 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_6_7 and 4 other fieldsHigh correlation
pay_5_-1 is highly correlated with pay_5_0 and 6 other fieldsHigh correlation
pay_3_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_6_-1 and 7 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 5 other fieldsHigh correlation
pay_1_2 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_0 is highly correlated with pay_2_0 and 7 other fieldsHigh correlation
education_5 is highly correlated with pay_6_7 and 67 other fieldsHigh correlation
pay_6_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_0 is highly correlated with pay_2_0 and 7 other fieldsHigh correlation
education_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_5_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_1_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_1_1 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1 and 2 other fieldsHigh correlation
pay_2_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_5 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 4 other fieldsHigh correlation
pay_5_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
education_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
education_6 is highly correlated with pay_6_7 and 67 other fieldsHigh correlation
pay_4_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
education_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_5_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
marriage_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
marriage_2 is highly correlated with marriage_1 and 2 other fieldsHigh correlation
pay_3_2 is highly correlated with pay_4_2 and 3 other fieldsHigh correlation
pay_4_-1 is highly correlated with pay_6_-1 and 7 other fieldsHigh correlation
education_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_2_0 and 7 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 4 other fieldsHigh correlation
pay_3_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_7 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_1_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_3_-1 and 4 other fieldsHigh correlation
pay_5_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_6_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 5 other fieldsHigh correlation
pay_2_1 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_6_7 and 4 other fieldsHigh correlation
pay_2_5 is highly correlated with pay_1_6 and 3 other fieldsHigh correlation
pay_6_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_5_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_5_7 is highly correlated with pay_6_7 and 4 other fieldsHigh correlation
pay_amt2 is highly skewed (γ1 = 30.45381745) Skewed
id is uniformly distributed Uniform
id has unique values Unique
bill_amt1 has 2008 (6.7%) zeros Zeros
bill_amt2 has 2506 (8.4%) zeros Zeros
bill_amt3 has 2870 (9.6%) zeros Zeros
bill_amt4 has 3195 (10.7%) zeros Zeros
bill_amt5 has 3506 (11.7%) zeros Zeros
bill_amt6 has 4020 (13.4%) zeros Zeros
pay_amt1 has 5249 (17.5%) zeros Zeros
pay_amt2 has 5396 (18.0%) zeros Zeros
pay_amt3 has 5968 (19.9%) zeros Zeros
pay_amt4 has 6408 (21.4%) zeros Zeros
pay_amt5 has 6703 (22.3%) zeros Zeros
pay_amt6 has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2021-11-08 22:34:58.961035
Analysis finished2021-11-08 22:36:35.330594
Duration1 minute and 36.37 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15000.5
Minimum1
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:35.468226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500.95
Q17500.75
median15000.5
Q322500.25
95-th percentile28500.05
Maximum30000
Range29999
Interquartile range (IQR)14999.5

Descriptive statistics

Standard deviation8660.398374
Coefficient of variation (CV)0.5773406469
Kurtosis-1.2
Mean15000.5
Median Absolute Deviation (MAD)7500
Skewness0
Sum450015000
Variance75002500
MonotonicityStrictly increasing
2021-11-08T16:36:35.608850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
199971
 
< 0.1%
200091
 
< 0.1%
200081
 
< 0.1%
200071
 
< 0.1%
200061
 
< 0.1%
200051
 
< 0.1%
200041
 
< 0.1%
200031
 
< 0.1%
200021
 
< 0.1%
Other values (29990)29990
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
300001
< 0.1%
299991
< 0.1%
299981
< 0.1%
299971
< 0.1%
299961
< 0.1%
299951
< 0.1%
299941
< 0.1%
299931
< 0.1%
299921
< 0.1%
299911
< 0.1%

limit_bal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:35.753253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2021-11-08T16:36:35.899865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:36.049435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
MonotonicityNot monotonic
2021-11-08T16:36:36.191051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291605
 
5.3%
271477
 
4.9%
281409
 
4.7%
301395
 
4.7%
261256
 
4.2%
311217
 
4.1%
251186
 
4.0%
341162
 
3.9%
321158
 
3.9%
331146
 
3.8%
Other values (46)16989
56.6%
ValueCountFrequency (%)
2167
 
0.2%
22560
 
1.9%
23931
3.1%
241127
3.8%
251186
4.0%
261256
4.2%
271477
4.9%
281409
4.7%
291605
5.3%
301395
4.7%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

bill_amt1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2021-11-08T16:36:36.334553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2021-11-08T16:36:36.478240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
50025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

bill_amt2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2021-11-08T16:36:36.624752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2021-11-08T16:36:36.767815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
32675
 
0.2%
78075
 
0.2%
31672
 
0.2%
39651
 
0.2%
250051
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

bill_amt3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-11-08T16:36:36.911431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2021-11-08T16:36:37.055013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

bill_amt4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2021-11-08T16:36:37.204645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2021-11-08T16:36:37.349263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
240039
 
0.1%
15039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

bill_amt5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-11-08T16:36:37.492876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2021-11-08T16:36:37.632539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

bill_amt6
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2021-11-08T16:36:37.769147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2021-11-08T16:36:37.931013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

pay_amt1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:38.081028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2021-11-08T16:36:38.213620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

pay_amt2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:38.348261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2021-11-08T16:36:38.487885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

pay_amt3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:38.634494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2021-11-08T16:36:38.766148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

pay_amt4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:38.903275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2021-11-08T16:36:39.037622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

pay_amt5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:39.180240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2021-11-08T16:36:39.313062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

pay_amt6
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-08T16:36:39.450130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2021-11-08T16:36:39.599355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

default_payment_next_month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Length

2021-11-08T16:36:39.830243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:39.885098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

pay_1_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
24314 
1
5686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Length

2021-11-08T16:36:40.481469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:40.548290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring characters

ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

pay_1_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
15263 
1
14737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Length

2021-11-08T16:36:40.718484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:40.785306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring characters

ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

pay_1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
26312 
1
3688 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Length

2021-11-08T16:36:40.954888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:41.024669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring characters

ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

pay_1_2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
27333 
1
 
2667

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Length

2021-11-08T16:36:41.190944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:41.266725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring characters

ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

pay_1_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29678 
1
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Length

2021-11-08T16:36:41.443251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:41.519082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring characters

ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

pay_1_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29924 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Length

2021-11-08T16:36:41.690181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:41.758017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

pay_1_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29974 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Length

2021-11-08T16:36:41.931419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:41.999240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

pay_1_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29989 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Length

2021-11-08T16:36:42.177581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:42.244402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

pay_1_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29991 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Length

2021-11-08T16:36:42.416268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:42.836149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

pay_1_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29981 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Length

2021-11-08T16:36:43.025858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:43.093679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

pay_2_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
23950 
1
6050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Length

2021-11-08T16:36:43.268212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:43.338031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring characters

ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

pay_2_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
15730 
0
14270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Length

2021-11-08T16:36:43.522532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:43.592347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring characters

ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

pay_2_1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29972 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Length

2021-11-08T16:36:43.766614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:43.835465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

pay_2_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
26073 
1
3927 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Length

2021-11-08T16:36:44.000690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:44.073531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring characters

ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

pay_2_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29674 
1
 
326

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Length

2021-11-08T16:36:44.245943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:44.333064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring characters

ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

pay_2_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29901 
1
 
99

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Length

2021-11-08T16:36:44.748350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:44.815208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

pay_2_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29975 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Length

2021-11-08T16:36:44.989716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:45.057528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

pay_2_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29988 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Length

2021-11-08T16:36:45.230956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:45.300771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

pay_2_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29980 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Length

2021-11-08T16:36:45.476362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:45.544210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

pay_2_8
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Length

2021-11-08T16:36:45.724745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:45.801489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

pay_3_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
24062 
1
5938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Length

2021-11-08T16:36:46.015916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:46.101688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring characters

ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

pay_3_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
15764 
0
14236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Length

2021-11-08T16:36:46.335096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:46.477692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring characters

ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

pay_3_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Length

2021-11-08T16:36:46.763833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:46.875538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

pay_3_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
26181 
1
3819 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Length

2021-11-08T16:36:47.098604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:47.175399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring characters

ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

pay_3_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29760 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Length

2021-11-08T16:36:47.380706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:47.531306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring characters

ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

pay_3_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29924 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Length

2021-11-08T16:36:47.733276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:47.804092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

pay_3_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29979 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Length

2021-11-08T16:36:48.067854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:48.182580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

pay_3_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29977 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Length

2021-11-08T16:36:48.362360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:48.430179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

pay_3_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29973 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Length

2021-11-08T16:36:48.608654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:48.677480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

pay_3_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29997 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Length

2021-11-08T16:36:48.859477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:48.940290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

pay_4_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
24313 
1
5687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Length

2021-11-08T16:36:49.263916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:49.414514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring characters

ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

pay_4_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
16455 
0
13545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Length

2021-11-08T16:36:49.619774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:49.695561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring characters

ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

pay_4_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-08T16:36:49.895947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:49.966761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

pay_4_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
26841 
1
3159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Length

2021-11-08T16:36:50.181232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:50.323876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring characters

ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

pay_4_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29820 
1
 
180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Length

2021-11-08T16:36:50.604104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:50.691839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

pay_4_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29931 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Length

2021-11-08T16:36:51.035920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:51.184547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

pay_4_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29965 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Length

2021-11-08T16:36:51.462773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:51.537573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

pay_4_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29995 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Length

2021-11-08T16:36:51.709731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:51.777549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

pay_4_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29942 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Length

2021-11-08T16:36:51.964340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:52.053091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

pay_4_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-08T16:36:52.237364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:52.306182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

pay_5_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
24461 
1
5539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Length

2021-11-08T16:36:52.509093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:52.583905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring characters

ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

pay_5_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
16947 
0
13053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Length

2021-11-08T16:36:52.785772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:52.859539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring characters

ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

pay_5_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
27374 
1
 
2626

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Length

2021-11-08T16:36:53.033022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:53.100873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring characters

ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

pay_5_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29822 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Length

2021-11-08T16:36:53.277736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:53.344587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

pay_5_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29916 
1
 
84

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Length

2021-11-08T16:36:54.005526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:54.082321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

pay_5_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29983 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Length

2021-11-08T16:36:54.258892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:54.326713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

pay_5_6
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Length

2021-11-08T16:36:54.502180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:54.569001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

pay_5_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29942 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Length

2021-11-08T16:36:54.743836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:54.841574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

pay_5_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Length

2021-11-08T16:36:55.235521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:55.301345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

pay_6_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
24260 
1
5740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Length

2021-11-08T16:36:55.490247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:55.558099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring characters

ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

pay_6_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
16286 
0
13714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Length

2021-11-08T16:36:55.726222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:55.791046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring characters

ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

pay_6_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
27234 
1
2766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Length

2021-11-08T16:36:55.958702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:56.026523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring characters

ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

pay_6_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29816 
1
 
184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Length

2021-11-08T16:36:56.198065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:56.268878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

pay_6_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29951 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Length

2021-11-08T16:36:56.440002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:56.510849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

pay_6_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29987 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Length

2021-11-08T16:36:56.686346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:56.753167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

pay_6_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29981 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Length

2021-11-08T16:36:56.925060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:56.992914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

pay_6_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29954 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Length

2021-11-08T16:36:57.168721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:57.235536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

pay_6_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-08T16:36:57.407844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:57.477659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

female
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
18112 
0
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Length

2021-11-08T16:36:57.655439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:57.726274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring characters

ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

education_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
19415 
1
10585 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Length

2021-11-08T16:36:57.903771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:57.976576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring characters

ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

education_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
15970 
1
14030 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Length

2021-11-08T16:36:58.162952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:58.230773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring characters

ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

education_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
25083 
1
4917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Length

2021-11-08T16:36:58.436612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:58.505460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring characters

ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

education_4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29546 
1
 
454

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Length

2021-11-08T16:36:58.678725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:58.745549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring characters

ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

education_5
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
30000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030000
100.0%

Length

2021-11-08T16:36:58.915779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:58.981600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
030000
100.0%

Most occurring characters

ValueCountFrequency (%)
030000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030000
100.0%

education_6
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
30000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030000
100.0%

Length

2021-11-08T16:36:59.144708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:59.212529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
030000
100.0%

Most occurring characters

ValueCountFrequency (%)
030000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030000
100.0%

marriage_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
16341 
1
13659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Length

2021-11-08T16:36:59.370537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:59.437361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring characters

ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

marriage_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
15964 
0
14036 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Length

2021-11-08T16:36:59.626942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:59.692758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring characters

ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

marriage_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
29623 
1
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Length

2021-11-08T16:36:59.865302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T16:36:59.932123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring characters

ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Interactions

2021-11-08T16:36:03.748172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:03.893114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.017781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.156410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.280111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.404245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.531908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.658538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:04.787192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.023176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.149842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.277501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.396149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.520818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.639529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.762204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:05.880887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.004279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.125249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.249079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.371746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.502237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.623912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.739971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.865639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:06.981592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.113273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.231012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.351693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.490352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.620969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.743640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.866424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:07.994508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.119961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.243631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.371114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.490794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.733114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.875579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:08.993296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.122040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.238722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.361398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.479153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.602039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.731099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.856762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:09.978438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.107149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.230819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.360471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.485117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.605273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.736921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.856120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:10.987769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.107485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.237102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.362797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.486420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.609116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.730717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.850397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:11.971074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.088759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.217431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.342081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.459762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.585432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.706108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:12.930468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:13.199712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:13.506497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:13.631167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:13.753839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:13.880219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.018080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.144770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.274816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.403472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.542135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.668796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.807393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:14.961775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.083446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.219083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.342273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.474034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.599731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.726392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:15.851643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.007259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.126579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.247939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.365657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.490290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.608490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.725280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.851849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:16.967540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.090211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.206688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.328363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.444087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.560776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.681421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.799106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:17.914796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.032646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.149315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.270077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.382780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.497451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.620107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.735830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:18.858500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.141146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.274820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.387522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.513150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.638845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.766487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:19.899453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.040075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.166583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.299228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.424651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.548323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.679935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.805599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:20.954202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.099845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.225503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.349718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.477172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.591872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.706558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.819818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:21.941491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.055220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.175981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.288682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.404336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.529005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.638742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.756570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.869972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:22.986694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:23.104688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:23.222405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:23.468714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:23.727021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:23.892579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.027219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.163865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.307433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.436089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.561631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.692316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.817978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:24.954612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.079245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.212988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.340646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.472293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.591008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.704688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.831097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:25.950401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.279667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.409351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.529031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.647682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.775557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:26.888288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.009928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.120716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.236717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.346391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.462081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.595760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.717609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.839298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:27.963931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.085596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.215286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.337921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.470066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.599170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.723792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.851833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:28.969519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.094186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.217854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.337567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.454255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.569786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.684893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.807565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:29.931235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.056369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.180293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.289370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.408049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.519720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.642401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.754911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.872631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:30.987322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.102672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.222386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.342269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.461982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.590605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.712314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.836709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:31.953363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.071081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.194717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.307449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.432116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.545778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.666638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T16:36:32.782088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-08T16:37:00.155526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-08T16:37:01.624563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-08T16:37:03.077673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-08T16:37:04.534771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-08T16:37:06.253007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-08T16:36:33.485255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idlimit_balagebill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default_payment_next_monthpay_1_-1pay_1_0pay_1_1pay_1_2pay_1_3pay_1_4pay_1_5pay_1_6pay_1_7pay_1_8pay_2_-1pay_2_0pay_2_1pay_2_2pay_2_3pay_2_4pay_2_5pay_2_6pay_2_7pay_2_8pay_3_-1pay_3_0pay_3_1pay_3_2pay_3_3pay_3_4pay_3_5pay_3_6pay_3_7pay_3_8pay_4_-1pay_4_0pay_4_1pay_4_2pay_4_3pay_4_4pay_4_5pay_4_6pay_4_7pay_4_8pay_5_-1pay_5_0pay_5_2pay_5_3pay_5_4pay_5_5pay_5_6pay_5_7pay_5_8pay_6_-1pay_6_0pay_6_2pay_6_3pay_6_4pay_6_5pay_6_6pay_6_7pay_6_8femaleeducation_1education_2education_3education_4education_5education_6marriage_1marriage_2marriage_3
0120000.0243913.03102.0689.00.00.00.00.0689.00.00.00.00.0100010000000001000000100000000010000000000000000000000000001010000100
12120000.0262682.01725.02682.03272.03455.03261.00.01000.01000.01000.00.02000.0110000000000001000000010000000001000000000100000000010000001010000010
2390000.03429239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.0001000000000100000000010000000001000000000100000000100000001010000010
3450000.03746990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.0001000000000100000000010000000001000000000100000000100000001010000100
4550000.0578617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.0010000000000100000000100000000001000000000100000000100000000010000100
5650000.03764400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.0001000000000100000000010000000001000000000100000000100000000100000010
67500000.029367965.0412023.0445007.0542653.0483003.0473944.055000.040000.038000.020239.013750.013770.0001000000000100000000010000000001000000000100000000100000000100000010
78100000.02311876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.0001000000001000000000100000000001000000000100000001000000001010000010
89140000.02811285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.0001000000000100000000000100000001000000000100000000100000001001000100
91020000.0350.00.00.00.013007.013912.00.00.00.013007.01122.00.0000000000000000000000000000000000000000001000000001000000000001000010

Last rows

idlimit_balagebill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default_payment_next_monthpay_1_-1pay_1_0pay_1_1pay_1_2pay_1_3pay_1_4pay_1_5pay_1_6pay_1_7pay_1_8pay_2_-1pay_2_0pay_2_1pay_2_2pay_2_3pay_2_4pay_2_5pay_2_6pay_2_7pay_2_8pay_3_-1pay_3_0pay_3_1pay_3_2pay_3_3pay_3_4pay_3_5pay_3_6pay_3_7pay_3_8pay_4_-1pay_4_0pay_4_1pay_4_2pay_4_3pay_4_4pay_4_5pay_4_6pay_4_7pay_4_8pay_5_-1pay_5_0pay_5_2pay_5_3pay_5_4pay_5_5pay_5_6pay_5_7pay_5_8pay_6_-1pay_6_0pay_6_2pay_6_3pay_6_4pay_6_5pay_6_6pay_6_7pay_6_8femaleeducation_1education_2education_3education_4education_5education_6marriage_1marriage_2marriage_3
2999029991140000.041138325.0137142.0139110.0138262.049675.046121.06000.07000.04228.01505.02000.02000.0001000000000100000000010000000001000000000100000000100000000010000100
2999129992210000.0342500.02500.02500.02500.02500.02500.00.00.00.00.00.00.0100001000000001000000000100000000010000000010000000010000000010000100
299922999310000.0438802.010400.00.00.00.00.02000.00.00.00.00.00.0001000000000100000000010000000000000000000000000000000000000001000100
2999329994100000.0383042.01427.0102996.070626.069473.055004.02000.0111784.04000.03000.02000.02000.0001000000001000000000100000000001000000000100000000100000000100000010
299942999580000.03472557.077708.079384.077519.082607.081158.07000.03500.00.07000.00.04000.0100010000000001000000000100000000010000000010000000010000000010000010
2999529996220000.039188948.0192815.0208365.088004.031237.015980.08500.020000.05003.03047.05000.01000.0001000000000100000000010000000001000000000100000000100000000001000100
2999629997150000.0431683.01828.03502.08979.05190.00.01837.03526.08998.0129.00.00.0010000000001000000000100000000010000000000100000000100000000001000010
299972999830000.0373565.03356.02758.020878.020582.019357.00.00.022000.04200.02000.03100.0100000100000000100000000100000010000000000100000000100000000010000010
299982999980000.041-1645.078379.076304.052774.011855.048944.085900.03409.01178.01926.052964.01804.0100100000001000000000010000000001000000000100000001000000000001000100
299993000050000.04647929.048905.049764.036535.032428.015313.02078.01800.01430.01000.01000.01000.0101000000000100000000010000000001000000000100000000100000000010000100